# Profitable Bayesian implementation in one-shot mechanism settings

## TL;DR

This paper explores how a mechanism designer can increase profit in one-shot settings by inducing agents to adjust their types, surpassing traditional auction profit limits through optimal adjustments.

## Contribution

It introduces a novel approach where the designer uses optimal adjustment costs to achieve higher profits in Bayesian implementation, overcoming traditional limitations.

## Key findings

- Optimal adjustment costs enable higher profits for the designer.
- The approach surpasses traditional auction profit limits.
- A constructed example demonstrates the profit breakthrough.

## Abstract

In the mechanism design theory, a designer would like to implement a desired social choice function which specifies her favorite outcome for each possible profile of all agents' types. Traditionally, the designer may be in a dilemma in the sense that even if she is not satisfied with some outcome with low profit, she has to announce it because she must obey the mechanism designed by herself. In this paper, we investigate a case where the designer can induce each agent to adjust his type in a one-shot mechanism. We propose that for a profitable Bayesian implementable social choice function, the designer may escape from the above-mentioned dilemma by spending the optimal adjustment cost and obtain a higher profit. Finally, we construct an example to show that the designer can breakthrough the limit of expected profit which she can obtain at most in the traditional optimal auction model.